A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)

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Accession:140249
This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).
Reference:
1 . Halnes G, Augustinaite S, Heggelund P, Einevoll GT, Migliore M (2011) A multi-compartment model for interneurons in the dorsal lateral geniculate nucleus. PLoS Comput Biol 7:e1002160 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Thalamus lateral geniculate nucleus interneuron;
Channel(s): I L high threshold; I T low threshold; I CAN; I Sodium; I Mixed; I Potassium; I_AHP;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Active Dendrites; Detailed Neuronal Models; Rebound firing;
Implementer(s): Halnes, Geir [geir.halnes at nmbu.no];
Search NeuronDB for information about:  I L high threshold; I T low threshold; I CAN; I Sodium; I Mixed; I Potassium; I_AHP;
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dLGN_modelDB
readme.html
Cad.mod *
HH_traub.mod *
iahp.mod *
iar.mod *
ical.mod *
Ican.mod *
it2.mod *
091008A2.hoc *
fixnseg.hoc *
INmodel.hoc
INmodel.ses
mosinit.hoc
Parameters1.hoc
Parameters2.hoc
screenshot1.jpg
screenshot2.jpg
screenshot3.jpg
screenshot4.jpg
                            
{load_file("nrngui.hoc")}
objectvar save_window_, rvp_
objectvar scene_vector_[4]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
xpanel("RunControl", 0)
v_init = -65
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5
xvalue("Continue til","runStopAt", 1,"{continuerun(runStopAt) stoprun=1}", 1, 1 )
runStopIn = 1
xvalue("Continue for","runStopIn", 1,"{continuerun(t + runStopIn) stoprun=1}", 1, 1 )
xbutton("Single Step","steprun()")
t = 3000
xvalue("t","t", 2 )
tstop = 3000
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 1e-08
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 40
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
screen_update_invl = 0.05
xvalue("Scrn update invl","screen_update_invl", 1,"", 0, 1 )
realtime = 8.7
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(1290,30)
}
{
xpanel("IClamp[0] at soma(0.5)", 0)
xlabel("IClamp[0] at soma(0.5)")
stim.del = 1000
xvalue("del","stim.del", 1,"", 0, 1 )
stim.dur = 900
xvalue("dur","stim.dur", 1,"", 0, 1 )
stim.amp = 0.055
xvalue("amp","stim.amp", 1,"", 0, 1 )
stim.i = 0
xvalue("i","stim.i", 0,"", 0, 1 )
xpanel(1302,474)
}
{
save_window_ = new Graph(0)
save_window_.size(0,3000,-80,40)
scene_vector_[2] = save_window_
{save_window_.view(0, -80, 3000, 120, 858, 384, 300.6, 200.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("v(.5)", 1, 1, 0.8, 0.9, 2)
}
{
save_window_ = new PlotShape(0)
save_window_.size(-366.324,200.488,-277.361,289.734)
save_window_.variable("v")
scene_vector_[3] = save_window_
{save_window_.view(-366.324, -277.361, 566.812, 567.095, 972, 30, 200.7, 200.8)}
fast_flush_list.append(save_window_)
save_window_.save_name("fast_flush_list.")
}
objectvar scene_vector_[1]
{doNotify()}

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